Metadata-Version: 2.1
Name: ppdp-anonops
Version: 0.0.2
Summary: A package providing multiple anonymization methods for XES-event logs
Home-page: https://github.com/TheDevSchnitzel/PPDP-AnonOps
Author: Alexander 'DevSchnitzel' Schnitzler
Author-email: DevSchnitzel@outlook.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
Requires-Dist: kmodes (>=0.10.2)
Requires-Dist: pm4py (==1.2.10)
Requires-Dist: p-privacy-metadata (==0.0.4)
Requires-Dist: matplotlib (>=2.2.2)
Requires-Dist: numpy (>=1.18.1)
Requires-Dist: pycryptodome (==3.9.9)
Requires-Dist: scikit-learn (>=0.23.2)

## Introduction
This project implemets basic anonymization operations for event data which are used by process mining techniques.
The anonymization operations are formally explained in the following paper: https://www.researchgate.net/publication/342048551_Privacy-Preserving_Data_Publishing_in_Process_Mining

Ref: implemeted by "Alexander 'DevSchnitzel' Schnitzler" as part of his bachelor thesis at PADS group. 

## Python package
The implementation has been published as a standard Python package. Use the following command to install the corresponding Python package:

```shell
pip install ppdp-anonops
```
## Usage
Look at the following directory to see the samples of usage:
"ppdp-anonops/tests"




